{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exogenous Variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exogenous variables can provide additional information to greatly improve forecasting accuracy. Some examples include price or future promotions variables for demand forecasting, and weather data for electricity load forecast. In this notebook we show an example on how to add different types of exogenous variables to NeuralForecast models for making day-ahead hourly electricity price forecasts (EPF) for France and Belgium markets."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All NeuralForecast models are capable of incorporating exogenous variables to model the following conditional predictive distribution:\n",
    "$$\\mathbb{P}(\\mathbf{y}_{t+1:t+H} \\;|\\; \\mathbf{y}_{[:t]},\\; \\mathbf{x}^{(h)}_{[:t]},\\; \\mathbf{x}^{(f)}_{[:t+H]},\\; \\mathbf{x}^{(s)} )$$\n",
    "\n",
    "where the regressors are static exogenous $\\mathbf{x}^{(s)}$, historic exogenous $\\mathbf{x}^{(h)}_{[:t]}$, exogenous available at the time of the prediction $\\mathbf{x}^{(f)}_{[:t+H]}$ and autorregresive features $\\mathbf{y}_{[:t]}$. Depending on the [train loss](https://nixtla.github.io/neuralforecast/losses.pytorch.html), the model outputs can be point forecasts (location estimators) or uncertainty intervals (quantiles)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will show you how to include exogenous variables in the data, specify variables to a model, and produce forecasts using future exogenous variables."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "This Guide assumes basic knowledge on the NeuralForecast library. For a minimal example visit the [Getting Started](../getting-started/02_quickstart.ipynb) guide.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run these experiments using GPU with Google Colab.\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/Nixtla/neuralforecast/blob/main/nbs/examples/Exogenous_Variables.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install neuralforecast"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Load data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `df` dataframe contains the target and exogenous variables past information to train the model. The `unique_id` column identifies the markets, `ds` contains the datestamps, and `y` the electricity price.\n",
    "\n",
    "Include both historic and future temporal variables as columns. In this example, we are adding the system load (`system_load`) as historic data. For future variables, we include a forecast of how much electricity will be produced (`gen_forecast`) and day of week (`week_day`). Both the electricity system demand and offer impact the price significantly, including these variables to the model greatly improve performance, as we demonstrate in Olivares et al. (2022). \n",
    "\n",
    "The distinction between historic and future variables will be made later as parameters of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from utilsforecast.plotting import plot_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>gen_forecast</th>\n",
       "      <th>system_load</th>\n",
       "      <th>week_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 00:00:00</td>\n",
       "      <td>53.48</td>\n",
       "      <td>76905.0</td>\n",
       "      <td>74812.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 01:00:00</td>\n",
       "      <td>51.93</td>\n",
       "      <td>75492.0</td>\n",
       "      <td>71469.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 02:00:00</td>\n",
       "      <td>48.76</td>\n",
       "      <td>74394.0</td>\n",
       "      <td>69642.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 03:00:00</td>\n",
       "      <td>42.27</td>\n",
       "      <td>72639.0</td>\n",
       "      <td>66704.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2015-01-01 04:00:00</td>\n",
       "      <td>38.41</td>\n",
       "      <td>69347.0</td>\n",
       "      <td>65051.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds      y  gen_forecast  system_load  week_day\n",
       "0        FR 2015-01-01 00:00:00  53.48       76905.0      74812.0         3\n",
       "1        FR 2015-01-01 01:00:00  51.93       75492.0      71469.0         3\n",
       "2        FR 2015-01-01 02:00:00  48.76       74394.0      69642.0         3\n",
       "3        FR 2015-01-01 03:00:00  42.27       72639.0      66704.0         3\n",
       "4        FR 2015-01-01 04:00:00  38.41       69347.0      65051.0         3"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\n",
    "    'https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE.csv',\n",
    "    parse_dates=['ds'],\n",
    ")\n",
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "Calendar variables such as day of week, month, and year are very useful to capture long seasonalities.\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x350 with 2 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add the static variables in a separate `static_df` dataframe. In this example, we are using one-hot encoding of the electricity market. The `static_df` must include one observation (row) for each `unique_id` of the `df` dataframe, with the different statics variables as columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>market_0</th>\n",
       "      <th>market_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BR</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id  market_0  market_1\n",
       "0        FR         1         0\n",
       "1        BR         0         1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "static_df = pd.read_csv('https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE_static.csv')\n",
    "static_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Training with exogenous variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We distinguish the exogenous variables by whether they reflect static or time-dependent aspects of the modeled data.\n",
    "\n",
    "* **Static exogenous variables**: \n",
    "The static exogenous variables carry time-invariant information for each time series. When the model is built with global parameters to forecast multiple time series, these variables allow sharing information within groups of time series with similar static variable levels. Examples of static variables include designators such as identifiers of regions, groups of products, etc.\n",
    "\n",
    "* **Historic exogenous variables**:\n",
    "This time-dependent exogenous variable is restricted to past observed values. Its predictive power depends on Granger-causality, as its past values can provide significant information about future values of the target variable $\\mathbf{y}$.\n",
    "\n",
    "* **Future exogenous variables**: \n",
    "In contrast with historic exogenous variables, future values are available at the time of the prediction. Examples include calendar variables, weather forecasts, and known events that can cause large spikes and dips such as scheduled promotions."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To add exogenous variables to the model, first specify the name of each variable from the previous dataframes to the corresponding model hyperparameter during initialization: `futr_exog_list`, `hist_exog_list`, and `stat_exog_list`. We also set `horizon` as 24 to produce the next day hourly forecasts, and set `input_size` to use the last 5 days of data as input.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "\n",
    "from neuralforecast.auto import NHITS, BiTCN\n",
    "from neuralforecast.core import NeuralForecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.getLogger(\"pytorch_lightning\").setLevel(logging.WARNING)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 1\n",
      "Seed set to 1\n"
     ]
    }
   ],
   "source": [
    "horizon = 24 # day-ahead daily forecast\n",
    "models = [NHITS(h = horizon,\n",
    "                max_steps=100,\n",
    "                input_size = 5*horizon,\n",
    "                futr_exog_list = ['gen_forecast', 'week_day'], # <- Future exogenous variables\n",
    "                hist_exog_list = ['system_load'], # <- Historical exogenous variables\n",
    "                stat_exog_list = ['market_0', 'market_1'], # <- Static exogenous variables\n",
    "                scaler_type = 'robust'),\n",
    "          BiTCN(h = horizon,\n",
    "                input_size = 5*horizon,\n",
    "                max_steps=100,\n",
    "                futr_exog_list = ['gen_forecast', 'week_day'], # <- Future exogenous variables\n",
    "                hist_exog_list = ['system_load'], # <- Historical exogenous variables\n",
    "                stat_exog_list = ['market_0', 'market_1'], # <- Static exogenous variables\n",
    "                scaler_type = 'robust',\n",
    "                ),                \n",
    "                ]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-tip}\n",
    "When including exogenous variables always use a scaler by setting the `scaler_type` hyperparameter. The scaler will scale all the temporal features: the target variable `y`, historic and future variables.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Make sure future and historic variables are correctly placed. Defining historic variables as future variables will lead to data leakage.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, pass the datasets to the `df` and `static_df` inputs of the `fit` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nf = NeuralForecast(models=models, freq='h')\n",
    "nf.fit(df=df, static_df=static_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Forecasting with exogenous variables"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before predicting the prices, we need to gather the future exogenous variables for the day we want to forecast. Define a new dataframe (`futr_df`) with the `unique_id`, `ds`, and future exogenous variables. There is no need to add the target variable `y` and historic variables as they won't be used by the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>gen_forecast</th>\n",
       "      <th>week_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>49118.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>47890.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>47158.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>45991.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FR</td>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>45378.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds  gen_forecast  week_day\n",
       "0        FR 2016-11-01 00:00:00       49118.0         1\n",
       "1        FR 2016-11-01 01:00:00       47890.0         1\n",
       "2        FR 2016-11-01 02:00:00       47158.0         1\n",
       "3        FR 2016-11-01 03:00:00       45991.0         1\n",
       "4        FR 2016-11-01 04:00:00       45378.0         1"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "futr_df = pd.read_csv(\n",
    "    'https://datasets-nixtla.s3.amazonaws.com/EPF_FR_BE_futr.csv',\n",
    "    parse_dates=['ds'],\n",
    ")\n",
    "futr_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ":::{.callout-important}\n",
    "Make sure `futr_df` has informations for the entire forecast horizon. In this example, we are forecasting 24 hours ahead, so `futr_df` must have 24 rows for each time series.\n",
    ":::"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, use the `predict` method to forecast the day-ahead prices. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ddde98d4595f48bb82f00cea537e8b50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b424558327a440eca0cf8f21da58be06",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Predicting: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>NHITS</th>\n",
       "      <th>BiTCN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 00:00:00</td>\n",
       "      <td>35.297050</td>\n",
       "      <td>41.957176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 01:00:00</td>\n",
       "      <td>32.350044</td>\n",
       "      <td>39.419579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 02:00:00</td>\n",
       "      <td>30.091702</td>\n",
       "      <td>36.313972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 03:00:00</td>\n",
       "      <td>27.317764</td>\n",
       "      <td>34.002922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE</td>\n",
       "      <td>2016-11-01 04:00:00</td>\n",
       "      <td>24.316488</td>\n",
       "      <td>35.002541</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  unique_id                  ds      NHITS      BiTCN\n",
       "0        BE 2016-11-01 00:00:00  35.297050  41.957176\n",
       "1        BE 2016-11-01 01:00:00  32.350044  39.419579\n",
       "2        BE 2016-11-01 02:00:00  30.091702  36.313972\n",
       "3        BE 2016-11-01 03:00:00  27.317764  34.002922\n",
       "4        BE 2016-11-01 04:00:00  24.316488  35.002541"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_hat_df = nf.predict(futr_df=futr_df)\n",
    "Y_hat_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x350 with 2 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(df, Y_hat_df, max_insample_length=24*5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In summary, to add exogenous variables to a model make sure to follow the next steps:\n",
    "\n",
    "1. Add temporal exogenous variables as columns to the main dataframe (`df`).\n",
    "2. Add static exogenous variables with the `static_df` dataframe.\n",
    "3. Specify the name for each variable in the corresponding model hyperparameter.\n",
    "4. If the model uses future exogenous variables, pass the future dataframe (`futr_df`) to the `predict` method."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz, Rafał Weron, Artur Dubrawski, Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx, International Journal of Forecasting](https://www.sciencedirect.com/science/article/pii/S0169207022000413)\n",
    "\n",
    "- [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2021). NHITS: Neural Hierarchical Interpolation for Time Series Forecasting. Accepted at AAAI 2023.](https://arxiv.org/abs/2201.12886)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
